Rate-Aware Quantum-Inspired Trajectory Learning for Interference-Limited Multi-UAV Networks

• URL: http://arxiv.org/abs/2606.25480v1
• Date: Wed, 24 Jun 2026 07:10:37 GMT
• Title: Rate-Aware Quantum-Inspired Trajectory Learning for Interference-Limited Multi-UAV Networks
• Abstract summary: Unmanned aerial vehicle (UAV) can provide on-demand, high-capacity connectivity in disaster and normal situation.<n>We propose the Rate-Aware Quantum-Annealed Graph Condensation (RA-QAGC) scheme to enable scalable, interference-aware UAV coordination.
• Score: 1.2759527491524598
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unmanned aerial vehicle (UAV) can provide on-demand, high-capacity connectivity in disaster and normal situation. However, it faces a challenge of curse of dimensionality in trajectory optimization, where interference-limited environments and vast search spaces make real-time coordination computationally expensive. To overcome this challenge, we propose the Rate-Aware Quantum-Annealed Graph Condensation (RA-QAGC) scheme, which combines rate-aware graph abstraction with decentralized reinforcement learning to enable scalable, interference-aware UAV coordination. By identifying high throughput locations and guiding UAV trajectory adaptation toward throughput-optimal regions, RA-QAGC effectively balances network capacity by maintaining quality-of-service (QoS) requirements. Simulation results demonstrate the proposal outperformed over existing schemes by achieving 59.4 Mbps total throughput and 23.9 Mbps priority-user throughput, representing gains of approximately 15% and 34%, respectively, over the baseline schemes.

Related papers

• Secrecy Energy Efficiency for IRS-Assisted Low-Altitude Communications: A D3QN-PER Based Approach [25.752091807817816]
  Security and energy challenges in low-altitude economy (LAE) wireless communications are addressed.<n>We develop a secure synergistic network integrating unmanned aerial vehicle (UAV) and intelligent surface (IRS)<n>We show that the developed D3Q-PERN-based learning approaches outperforms existing state-of-the-art learning approaches.
  arXiv  Detail & Related papers  (2026-07-27T09:05:35Z)
• Trajectory Planning for UAV-Based Smart Farming Using Imitation-Based Triple Deep Q-Learning [5.160399918845654]
  We formulate the trajectory planning problem as a Markov decision process (MDP) and leverage multi-agent reinforcement learning (MARL) to solve it.<n>We propose a novel imitation-based triple deep Q-network (ITDQN) algorithm, which employs an elite imitation mechanism to reduce exploration costs.<n>Our proposed ITDQN outperforms DDQN by 4.43% in weed recognition rate and 6.94% in data collection rate.
  arXiv  Detail & Related papers  (2025-12-21T05:30:19Z)
• Joint UAV-UGV Positioning and Trajectory Planning via Meta A3C for Reliable Emergency Communications [5.254446611211724]
  Joint deployment of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) has been shown to be an effective method to establish communications in areas affected by disasters.<n>This paper proposes a joint UAV-UGV-based positioning and trajectory planning framework for UAVs and UGVs deployment that guarantees optimal for ground users.
  arXiv  Detail & Related papers  (2025-12-20T08:53:15Z)
• QoS-Aware Hierarchical Reinforcement Learning for Joint Link Selection and Trajectory Optimization in SAGIN-Supported UAV Mobility Management [52.15690855486153]
  A space-air-ground integrated network (SAGIN) has emerged as an essential architecture for enabling ubiquitous UAV connectivity.<n>This paper formulates UAV mobility management in SAGIN as a constrained multiobjective joint optimization problem.
  arXiv  Detail & Related papers  (2025-12-17T06:22:46Z)
• AirFed: Federated Graph-Enhanced Multi-Agent Reinforcement Learning for Multi-UAV Cooperative Mobile Edge Computing [21.4907371859268]
  Multiple Unmanned Aerial Vehicles (UAVs) cooperative Mobile Edge Computing (MEC) systems face critical challenges in coordinating trajectory planning, task offloading, and resource allocation.<n>Existing approaches suffer from limited scalability, slow convergence, and inefficient knowledge sharing among UAVs.<n>This paper proposes AirFed, a novel federated graph-enhanced multi-agent reinforcement learning framework.
  arXiv  Detail & Related papers  (2025-10-27T06:31:35Z)
• Latency Minimization for UAV-Enabled Federated Learning: Trajectory Design and Resource Allocation [47.20867891501245]
  Federated learning (FL) has become a transformative paradigm for distributed machine learning across wireless networks.<n>We propose a novel framework that employs an unmanned aerial vehicle (UAV) as a mobile server to enhance the FL training process.<n>We show that our proposed scheme not only surpasses existing benchmark schemes in reducing latency up to 15.29%, but also achieves training efficiency that nearly matches the ideal scenario.
  arXiv  Detail & Related papers  (2024-12-10T11:39:27Z)
• Goal-Oriented Semantic Communication for Wireless Visual Question Answering [68.75814200517854]
  We propose a goal-oriented semantic communication (GSC) framework to improve Visual Question Answering (VQA) performance.<n>We propose a bounding box (BBox)-based image semantic extraction and ranking approach to prioritize the semantic information based on the goal of questions.<n> Experimental results demonstrate that our GSC framework improves answering accuracy by up to 49% under AWGN channels and 59% under Rayleigh channels.
  arXiv  Detail & Related papers  (2024-11-03T12:01:18Z)
• Multi-Objective Optimization for UAV Swarm-Assisted IoT with Virtual Antenna Arrays [55.736718475856726]
  Unmanned aerial vehicle (UAV) network is a promising technology for assisting Internet-of-Things (IoT) Existing UAV-assisted data harvesting and dissemination schemes require UAVs to frequently fly between the IoTs and access points. We introduce collaborative beamforming into IoTs and UAVs simultaneously to achieve energy and time-efficient data harvesting and dissemination.
  arXiv  Detail & Related papers  (2023-08-03T02:49:50Z)
• Multi-Agent Reinforcement Learning in NOMA-aided UAV Networks for Cellular Offloading [59.32570888309133]
  A novel framework is proposed for cellular offloading with the aid of multiple unmanned aerial vehicles (UAVs) Non-orthogonal multiple access (NOMA) technique is employed at each UAV to further improve the spectrum efficiency of the wireless network. A mutual deep Q-network (MDQN) algorithm is proposed to jointly determine the optimal 3D trajectory and power allocation of UAVs.
  arXiv  Detail & Related papers  (2020-10-18T20:22:05Z)
• NOMA in UAV-aided cellular offloading: A machine learning approach [59.32570888309133]
  A novel framework is proposed for cellular offloading with the aid of multiple unmanned aerial vehicles (UAVs) Non-orthogonal multiple access (NOMA) technique is employed at each UAV to further improve the spectrum efficiency of the wireless network. A mutual deep Q-network (MDQN) algorithm is proposed to jointly determine the optimal 3D trajectory and power allocation of UAVs.
  arXiv  Detail & Related papers  (2020-10-18T17:38:48Z)
• Data Freshness and Energy-Efficient UAV Navigation Optimization: A Deep Reinforcement Learning Approach [88.45509934702913]
  We design a navigation policy for multiple unmanned aerial vehicles (UAVs) where mobile base stations (BSs) are deployed. We incorporate different contextual information such as energy and age of information (AoI) constraints to ensure the data freshness at the ground BS. By applying the proposed trained model, an effective real-time trajectory policy for the UAV-BSs captures the observable network states over time.
  arXiv  Detail & Related papers  (2020-02-21T07:29:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.